The quest for ubiquitous and broadband wireless access is driving the deployment of wideband signals with high peak-to-average power ratios (PAPRs). These signals pose several challenges to efficiency and linearity in radio frequency (RF) transmitters, particularly within the power amplifier (PA) stage. This is mainly attributed to the intensification of the distortion effects due to the static nonlinearity and memory effects (MEs) exhibited by a PA as the bandwidth and PAPR grow. Digital pre-distortion (DPD) techniques are often used to mitigate distortions exhibited by PAs. As shown in FIG. 1, the goal of a DPD engine 12 is to pre-distort the input signal to compensate for the distortion caused by a power amplifier 14 so that the output of the power amplifier 14 is an accurate amplified version of the input signal. These DPD techniques have succeeded to a great extent in improving the achievable PA linearity versus power efficiency trade-off.
Nonlinear PA behavioral models and DPD schemes have been classified into two categories: memory-less models and models with memory. The application of memory-less models/DPDs has been limited to narrow band scenarios where the distortions are mainly attributable to a transistor's static nonlinearity. However, as the bandwidth of input signals has broadened, PA models/DPDs developed into more comprehensive, nonlinear, and memory-capable modeling schemes capable of accounting for both MEs and static nonlinearity.
Spurred by the Volterra series, a number of PA models/DPDs with memory have been devised to reduce implementation burden. These include the dynamic derivation reduction (DDR) based Volterra series, generalized memory polynomial (GMP) methods and the memory polynomial model. Yet, the complexity of the previously mentioned DPD schemes escalates as the bandwidth of the deployed signal broadens, rendering their practical implementation exceedingly challenging. The problems extend to three different components of the DPD system 15, as shown in FIG. 2 namely:
the digital predistortion engine 16;
the digital predistortion parameters identifier 18; and
the transmitter observation receiver 20.
Deployment of wideband signals necessitates the use of high speed DPD engines 16, and digital to analog converters (DACs) 22. In addition, these signals require a transmitter observation receiver (TOR) 20 with RF-to-IF frequency conversion module 30, band pass filter 34 and high-speed analog to digital converters (ADC) 24. The high sampling rate of the ADC 24 of at least 5 times the input Nyquist rate (5×INR) of the input signal bandwidth was believed to be required for capturing accurate measurement data for the identification of the DPD parameters and linearization capability. The significant power and cost overheads of high-speed ADCs, DACs, and DPD processing engines bring down the overall efficiency of the DPD+PA cascade and limits the usefulness of the DPD for enhancing the trade-off between efficiency and linearity.
The system 15 of FIG. 2 also includes an up-converter 26 that up-converts the analog output of the DAC 22 to RF, and a power amplifier 28 that amplifies the up-converted analog signal. In the TOR 20, a first down converter 30 down converts the RF power amplifier output to an intermediate frequency IF, a second down converter 32 to convert the IF signal to baseband. In some cases, a band pass filter 34 filters the power amplifier output, which defines the observation bandwidth of the TOR 20, and is added to reject any potential aliasing.
Several attempts have been reported to relax the transmitter observation receiver (TOR) bandwidth and consequently, the required analog to digital converter sampling rate in the TOR, and to alleviate the complexity of the DPD implementation as the signal bandwidth broadens. The generalized sampling theorem (GST) has been used to reconstruct the output signal of a nonlinear system using a TOR with reduced bandwidth. The GST calls for an additional function which is used to reduce the bandwidth of the PA output signal as well as inversing the nonlinear system behavior. According to some, the application of the GST in PA behavioral modeling problems requires a priori knowledge of its corresponding DPD function so that the PA output signal can be sampled using the input Nyquist rate (INR), i.e., twice the input signal bandwidth, rather than the output Nyquist rate (ONR), i.e., twice the output signal bandwidth. However, the a priori knowledge of the precise DPD is practically unattainable.
A band-limited Volterra series model to restrict the linearization application to the available observation path bandwidth has also been proposed. Using the band-limited Volterra series, it has been shown that linearization was successful within the observation band of the TOR. The residual spectrum regrowth introduced by the PA outside the observation bandwidth and not tackled by the band-limited Volterra series DPD is mitigated using a sharp band-pass filter (BPF) at the output of the power amplifier. The high-order of the BPF would imply non-negligible insertion loss, which should be included in order to carefully assess the potential performance degradation. However, the band-limited Volterra DPD does not allow for complete linearization of PA output inter-modulation distortions (IMDs).